A robust adaptive controller for a three-phase three-level neutral-point clamped rectifier
Bibliographic record
Abstract
This paper presents a new adaptive strategy to control the DC output voltage and the power factor of a three-phase, three-level, neutral point clamped (NPC) boost rectifier. The proposed controller combines three sub-controllers. The zero-sequence controller maintains the difference between the upper and the lower DC output voltages equal to zero. The q-axis controller keeps the power factor equal to 1. The d-axis controller is used to meet active power and DC output voltage specifications, despite balanced or unbalanced load conditions. Converter and AC voltage source parameters (amplitude and frequency) are assumed unknown. A robust adaptive nonlinear control law is derived from a backstepping procedure and helps to stabilize the system and attenuate unknown parameters effects. The adaptation laws are based on the projection method and guarantee that estimated parameters converge and remain inside predefined domains. The main advantage of the proposed design approach is its simplicity due to the fact that sub-controller designs are decoupled, and their gains are independently tuned. Simulation results demonstrate the effectiveness and the performance of the new robust adaptive controller.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".